Gerhard Kloesch

Computer-assisted sleep classification according to the standard of the American Academy of Sleep Medicine: validation study of the AASM version of the Somnolyzer 24 × 7.

Background: In 2007, the AASM Manual for the Scoring of Sleep and Associated Events was published by the American Academy of Sleep Medicine (AASM). Concerning the visual classification of sleep stages, these new rules are intended to replace the rules by Rechtschaffen and Kales (R&K). Methods: We adapted the automatic R&K sleep scoring system Somnolyzer 24 × 7 to comply with the AASM rules and subsequently performed a validation study based on 72 polysomnographies from the Siesta database (56 healthy subjects, 16 patients, 38 females, 34 males, aged 21–86 years). Scorings according to the AASM rules were performed manually by experienced sleep scorers and semi-automatically by the AASM version of the Somnolyzer. Manual scorings and Somnolyzer reviews were performed independently by at least 2 out of 8 experts from 4 sleep centers. Results: In the quality control process, sleep experts corrected 4.8 and 3.7% of the automatically assigned epochs, resulting in a reliability between 2 Somnolyzer-assisted scorings of 99% (Cohen’s kappa: 0.99). In contrast, the reliability between the 2 manual scorings was 82% (kappa: 0.76). The agreement between the 2 Somnolyzer-assisted and the 2 visual scorings was between 81% (kappa: 0.75) and 82% (kappa: 0.76). Conclusion: The AASM version of the Somnolyzer revealed an agreement between semi-automated and human expert scoring comparable to that published for the R&K version with a validity comparable to that of human experts, but with a reliability close to 1, thereby reducing interrater variability as well as scoring time to a minimum.

“Diagnosis by Behavioral Observation” Home-Videosomnography – A Rigorous Ethnographic Approach to Sleep of Children with Neurodevelopmental Conditions

Introduction: Advanced video technology is available for sleep-laboratories. However, low-cost equipment for screening in the home setting has not been identified and tested, nor has a methodology for analysis of video recordings been suggested. Methods: We investigated different combinations of hardware/software for home-videosomnography (HVS) and established a process for qualitative and quantitative analysis of HVS-recordings. A case vignette (HVS analysis for a 5.5-year-old girl with major insomnia and several co-morbidities) demonstrates how methodological considerations were addressed and how HVS added value to clinical assessment. Results: We suggest an “ideal set of hardware/software” that is reliable, affordable (∼

3D detection of periodic limb movements in sleep

500) and portable (=2.8 kg) to conduct non-invasive HVS, which allows time-lapse analyses. The equipment consists of a net-book, a camera with infrared optics, and a video capture device. (1) We present an HVS-analysis protocol consisting of three steps of analysis at varying replay speeds: (a) basic overview and classification at 16× normal speed; (b) second viewing and detailed descriptions at 4–8× normal speed, and (c) viewing, listening, and in-depth descriptions at real-time speed. (2) We also present a custom software program that facilitates video analysis and note-taking (Annotator©), and Optical Flow software that automatically quantifies movement for internal quality control of the HVS-recording. The case vignette demonstrates how the HVS-recordings revealed the dimension of insomnia caused by restless legs syndrome, and illustrated the cascade of symptoms, challenging behaviors, and resulting medications. Conclusion: The strategy of using HVS, although requiring validation and reliability testing, opens the floor for a new “observational sleep medicine,” which has been useful in describing discomfort-related behavioral movement patterns in patients with communication difficulties presenting with challenging/disruptive sleep/wake behaviors.

Contactless 3D detection of respiratory effort

The standard polysomnographic method for detecting periodic limb movements in sleep (PLMS) includes measuring the electromyography (EMG) signals from electrodes at the left and right tibialis anterior muscles. This procedure has disadvantages as the cabling affects the patients quality of sleep and the electrodes tend to come off during the night, deteriorating data quality. We used contactless monitoring of body movements by a 3D time-of-flight camera mounted above the bed. Changes in the 3D silhouette indicate motion. Contactless detection of PLMS has several substantial advantages over the EMG and provides more complete and more specific diagnostic data: (1) Motor events caused by other leg muscles than tibialis anterior muscles are fully captured by the 3D method, but missed by EMG. (2) 3D does not react to tonic muscle contractions, where such contractions cause strong deflections in EMG which are annotated as limb movements by most PSG apparatus. Another aspect turned out to be of high practical relevance: Deflections in EMG traces are frequently caused by poor electrode contacts, potentially causing false movement annotations. This can lead to substantial overestimation of the automatically computed PLM index. Contactless sensing completely avoids such problems.The standard polysomnographic method for detecting periodic limb movements in sleep (PLMS) includes measuring the electromyography (EMG) signals from electrodes at the left and right tibialis anterior muscles. This procedure has disadvantages as the cabling affects the patients quality of sleep and the electrodes tend to come off during the night, deteriorating data quality. We used contactless monitoring of body movements by a 3D time-of-flight camera mounted above the bed. Changes in the 3D silhouette indicate motion. Contactless detection of PLMS has several substantial advantages over the EMG and provides more complete and more specific diagnostic data: (1) Motor events caused by other leg muscles than tibialis anterior muscles are fully captured by the 3D method, but missed by EMG. (2) 3D does not react to tonic muscle contractions, where such contractions cause strong deflections in EMG which are annotated as limb movements by most PSG apparatus. Another aspect turned out to be of high practical relevance: Deflections in EMG traces are frequently caused by poor electrode contacts, potentially causing false movement annotations. This can lead to substantial overestimation of the automatically computed PLM index. Contactless sensing completely avoids such problems.

3D detection of the central sleep apnoea syndrome

Respiratory effort is a major feature for detection and classification of apneas in polysomnography. Presently, somnologists apply flow sensors and/or rip belts at the thorax and abdomen for this purpose, causing practical problems with the montage and re-adjustment during the night and disturbing patients´ sleep. Contactless measurements would be a desirable alternative. We utilized a 3D time-of-flight camera to monitor respiratory-related chest movements to decipher epochs of normal breathing and apnea. Time-synchronized comparisons of 3D measurements of chest movements due to respiration to signals from rip belts and nasal airflow proved that the 3D sensor provided equivalent results. This new technique could support the diagnosis of sleep apnea and Cheyne-Stokes breathing. It simplifies the procedure, saves personnel capacity, improves data quality and releases the burden to the patient by replacing body-mounted sensors and cabling.

Contents Vol. 62, 2010

Abstract In polysomnography, an oronasal thermal airflow sensor and respiratory inductance plethysmography (RIP) belts at thorax and abdomen are used to detect central sleep apnoea. These sensors are uncomfortable to wear, can disturb the patient’s sleep, and data quality can be significantly di-minished if a sensor slips off the patient. Contactless meas-urements would be a desirable alternative. We utilized a 3D time-of-flight sensor to monitor respiratory-related chest movements to decipher epochs of normal breathing and ap-noea in ten adult patients with a total of 467 apnoea events. Time-synchronized comparisons of 3D measurements of chest movements due to respiration to polysomnography signals from rip belts and nasal airflow proved that the 3D sensor provided largely equivalent results. This new tech-nique could support the diagnosis of central sleep apnoea and Cheyne-Stokes respiration.

Sleep Classification According to AASM and Rechtschaffen & Kales: Effects on Sleep Scoring Parameters

A. Drago, Naples G. Erdmann, Berlin A. Fischer, Göttingen J.M. Ford, San Francisco, Calif. S. Galderisi, Naples M. Hatzinger, Solothurn U. Hegerl, Leipzig K. Hirata, Mibu M. Kato, Osaka J. Kornhuber, Erlangen D. Lehmann, Zürich P. Monteleone, Naples G. Okugawa, Osaka G.N. Papadimitriou, Athens M. Popoli, Milano M. Reuter, Bonn F. Rösler, Marburg G. Ruigt, Oss J.K. Rybakowski, Poznan F. Schneider, Aachen R. Schwarting, Marburg M. Shigeta, Tokyo D. Souery, Brussels A. Steiger, Munich P. Willner, Swansea Associate Editors